OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Inference in Markov Control Problems
Venue: arXiv cs.SY 2012
Year: 2012

Markov control algorithms that perform smooth, non-greedy updates of the policy have been shown to be very general and versatile, with policy gradient and Expectation Maximisation algorithms being particularly popular. For these algorithms, marginal inference of the reward weighted trajectory distribution is required to perform policy updates. We discuss a new exact inference algorithm for these marginals in the finite horizon case that is more efficient than the standard approach based on classical forward-backward recursions. We also provide a principled extension to infinite horizon Markov Decision Problems that explicitly accounts for an infinite horizon. This extension provides a novel algorithm for both policy gradients and Expectation Maximisation in infinite horizon problems.
Variational Algorithms for Marginal MAP
Venue: arXiv cs.LG 2012
Year: 2012

Marginal MAP problems are notoriously difficult tasks for graphical models. We derive a general variational framework for solving marginal MAP problems, in which we apply analogues of the Bethe, tree-reweighted, and mean field approximations. We then derive a "mixed" message passing algorithm and a convergent alternative using CCCP to solve the BP-type approximations. Theoretically, we give conditions under which the decoded solution is a global or local optimum, and obtain novel upper bounds on solutions. Experimentally we demonstrate that our algorithms outperform related approaches. We also show that EM and variational EM comprise a special case of our framework.
Order-of-Magnitude Influence Diagrams
Venue: arXiv cs.AI 2012
Year: 2012

In this paper, we develop a qualitative theory of influence diagrams that can be used to model and solve sequential decision making tasks when only qualitative (or imprecise) information is available. Our approach is based on an order-of-magnitude approximation of both probabilities and utilities and allows for specifying partially ordered preferences via sets of utility values. We also propose a dedicated variable elimination algorithm that can be applied for solving order-of-magnitude influence diagrams.
Partial Order MCMC for Structure Discovery in Bayesian Networks
Venue: arXiv cs.LG 2012
Year: 2012

We present a new Markov chain Monte Carlo method for estimating posterior probabilities of structural features in Bayesian networks. The method draws samples from the posterior distribution of partial orders on the nodes; for each sampled partial order, the conditional probabilities of interest are computed exactly. We give both analytical and empirical results that suggest the superiority of the new method compared to previous methods, which sample either directed acyclic graphs or linear orders on the nodes.
Identifiability of Causal Graphs using Functional Models
Venue: arXiv cs.LG 2012
Year: 2012

This work addresses the following question: Under what assumptions on the data generating process can one infer the causal graph from the joint distribution? The approach taken by conditional independence-based causal discovery methods is based on two assumptions: the Markov condition and faithfulness. It has been shown that under these assumptions the causal graph can be identified up to Markov equivalence (some arrows remain undirected) using methods like the PC algorithm. In this work we propose an alternative by defining Identifiable Functional Model Classes (IFMOCs). As our main theorem we prove that if the data generating process belongs to an IFMOC, one can identify the complete causal graph. To the best of our knowledge this is the first identifiability result of this kind that is not limited to linear functional relationships. We discuss how the IFMOC assumption and the Markov and faithfulness assumptions relate to each other and explain why we believe that the IFMOC assumption can be tested more easily on given data. We further provide a practical algorithm that recovers the causal graph from finitely many data; experiments on simulated data support the theoretical findings.
Robust learning Bayesian networks for prior belief
Venue: arXiv cs.LG 2012
Year: 2012

Recent reports have described that learning Bayesian networks are highly sensitive to the chosen equivalent sample size (ESS) in the Bayesian Dirichlet equivalence uniform (BDeu). This sensitivity often engenders some unstable or undesirable results. This paper describes some asymptotic analyses of BDeu to explain the reasons for the sensitivity and its effects. Furthermore, this paper presents a proposal for a robust learning score for ESS by eliminating the sensitive factors from the approximation of log-BDeu.
Measuring the Hardness of Stochastic Sampling on Bayesian Networks with Deterministic Causalities: the k-Test
Venue: arXiv cs.AI 2012
Year: 2012

Approximate Bayesian inference is NP-hard. Dagum and Luby defined the Local Variance Bound (LVB) to measure the approximation hardness of Bayesian inference on Bayesian networks, assuming the networks model strictly positive joint probability distributions, i.e. zero probabilities are not permitted. This paper introduces the k-test to measure the approximation hardness of inference on Bayesian networks with deterministic causalities in the probability distribution, i.e. when zero conditional probabilities are permitted. Approximation by stochastic sampling is a widely-used inference method that is known to suffer from inefficiencies due to sample rejection. The k-test predicts when rejection rates of stochastic sampling a Bayesian network will be low, modest, high, or when sampling is intractable.
Sparse Topical Coding
Venue: arXiv cs.LG 2012
Year: 2012

We present sparse topical coding (STC), a non-probabilistic formulation of topic models for discovering latent representations of large collections of data. Unlike probabilistic topic models, STC relaxes the normalization constraint of admixture proportions and the constraint of defining a normalized likelihood function. Such relaxations make STC amenable to: 1) directly control the sparsity of inferred representations by using sparsity-inducing regularizers; 2) be seamlessly integrated with a convex error function (e.g., SVM hinge loss) for supervised learning; and 3) be efficiently learned with a simply structured coordinate descent algorithm. Our results demonstrate the advantages of STC and supervised MedSTC on identifying topical meanings of words and improving classification accuracy and time efficiency.
Exact Algorithms for Solving Stochastic Games
Venue: arXiv cs.GT 2012
Year: 2012

Shapley's discounted stochastic games, Everett's recursive games and Gillette's undiscounted stochastic games are classical models of game theory describing two-player zero-sum games of potentially infinite duration. We describe algorithms for exactly solving these games.
Internet Topology over Time
Venue: arXiv cs.NI 2012
Year: 2012

There are few studies that look closely at how the topology of the Internet evolves over time; most focus on snapshots taken at a particular point in time. In this paper, we investigate the evolution of the topology of the Autonomous Systems graph of the Internet, examining how eight commonly-used topological measures change from January 2002 to January 2010. We find that the distributions of most of the measures remain unchanged, except for average path length and clustering coefficient. The average path length has slowly and steadily increased since 2005 and the average clustering coefficient has steadily declined. We hypothesize that these changes are due to changes in peering policies as the Internet evolves. We also investigate a surprising feature, namely that the maximum degree has changed little, an aspect that cannot be captured without modeling link deletion. Our results suggest that evaluating models of the Internet graph by comparing steady-state generated topologies to snapshots of the real data is reasonable for many measures. However, accurately matching time-variant properties is more difficult, as we demonstrate by evaluating ten well-known models against the 2010 data.
AdSplit: Separating smartphone advertising from applications
Venue: arXiv cs.OS 2012
Year: 2012

A wide variety of smartphone applications today rely on third-party advertising services, which provide libraries that are linked into the hosting application. This situation is undesirable for both the application author and the advertiser. Advertising libraries require additional permissions, resulting in additional permission requests to users. Likewise, a malicious application could simulate the behavior of the advertising library, forging the user's interaction and effectively stealing money from the advertiser. This paper describes AdSplit, where we extended Android to allow an application and its advertising to run as separate processes, under separate user-ids, eliminating the need for applications to request permissions on behalf of their advertising libraries. We also leverage mechanisms from Quire to allow the remote server to validate the authenticity of client-side behavior. In this paper, we quantify the degree of permission bloat caused by advertising, with a study of thousands of downloaded apps. AdSplit automatically recompiles apps to extract their ad services, and we measure minimal runtime overhead. We also observe that most ad libraries just embed an HTML widget within and describe how AdSplit can be designed with this in mind to avoid any need for ads to have native code.
PAR-Aware Large-Scale Multi-User MIMO-OFDM Downlink
Venue: arXiv cs.IT 2012
Year: 2012

We investigate an orthogonal frequency-division multiplexing (OFDM)-based downlink transmission scheme for large-scale multi-user (MU) multiple-input multiple-output (MIMO) wireless systems. The use of OFDM causes a high peak-to-average (power) ratio (PAR), which necessitates expensive and power-inefficient radio-frequency (RF) components at the base station. In this paper, we present a novel downlink transmission scheme, which exploits the massive degrees-of-freedom available in large-scale MU-MIMO-OFDM systems to achieve low PAR. Specifically, we propose to jointly perform MU precoding, OFDM modulation, and PAR reduction by solving a convex optimization problem. We develop a corresponding fast iterative truncation algorithm (FITRA) and show numerical results to demonstrate tremendous PAR-reduction capabilities. The significantly reduced linearity requirements eventually enable the use of low-cost RF components for the large-scale MU-MIMO-OFDM downlink.
An efficient polynomial time approximation scheme for load balancing on uniformly related machines
Venue: arXiv cs.DS 2012
Year: 2012

We consider basic problems of non-preemptive scheduling on uniformly related machines. For a given schedule, defined by a partition of the jobs into m subsets corresponding to the m machines, C_i denotes the completion time of machine i. Our goal is to find a schedule which minimizes or maximizes \sum_{i=1}^m C_i^p for a fixed value of p such that 0<p<\infty. For p>1 the minimization problem is equivalent to the well-known problem of minimizing the \ell_p norm of the vector of the completion times of the machines, and for 0<p<1 the maximization problem is of interest. Our main result is an efficient polynomial time approximation scheme (EPTAS) for each one of these problems. Our schemes use a non-standard application of the so-called shifting technique. We focus on the work (total size of jobs) assigned to each machine and introduce intervals of forbidden work. These intervals are defined so that the resulting effect on the goal function is sufficiently small. This allows the partition of the problem into sub-problems (with subsets of machines and jobs) whose solutions are combined into the final solution using dynamic programming. Our results are the first EPTAS's for this natural class of load balancing problems.
Cross-Document Pattern Matching
Venue: arXiv cs.DS 2012
Year: 2012

We study a new variant of the string matching problem called cross-document string matching, which is the problem of indexing a collection of documents to support an efficient search for a pattern in a selected document, where the pattern itself is a substring of another document. Several variants of this problem are considered, and efficient linear-space solutions are proposed with query time bounds that either do not depend at all on the pattern size or depend on it in a very limited way (doubly logarithmic). As a side result, we propose an improved solution to the weighted level ancestor problem.
Evaluating Wireless Proactive Routing Protocols under Scalability and Traffic Constraints
Venue: arXiv cs.NI 2012
Year: 2012

In this paper, we evaluate and analyze the impact of different network loads and varying no. of nodes on distance vector and link state routing algorithms. We select three well known proactive protocols; Destination Sequenced Distance Vector (DSDV) operates on distance vector routing, while Fisheye State Routing (FSR) and Optimized Link State Routing (OLSR) protocols are based on link state routing. Further, we evaluate and compare the effects on the performance of protocols by changing the routing strategies of routing algorithms. We also enhance selected protocols to achieve high performance. We take throughput, End-to-End Delay (E2ED) and Normalized Routing Load (NRL) as performance metrics for evaluation and comparison of chosen protocols both with default and enhanced versions. Based upon extensive simulations in NS-2, we compare and discuss performance trade-offs of the protocols, i.e., how a protocol achieves high packet delivery by paying some cost in the form of increased E2ED and/or routing overhead. FSR due to scope routing technique performs well in high data rates, while, OLSR is more scalable in denser networks due to limited retransmissions through Multi-Point Relays (MPRs).
McCall's Area Transformation versus the Integrated Impact Indicator (I3)
Venue: arXiv cs.DL 2012
Year: 2012

In a study entitled "Skewed Citation Distributions and Bias Factors: Solutions to two core problems with the journal impact factor," Mutz & Daniel (2012) propose (i) McCall's (1922) Area Transformation of the skewed citation distribution so that this data can be considered as normally distributed (Krus & Kennedy, 1977), and (ii) to control for different document types as a co-variate (Rubin, 1977). This approach provides an alternative to Leydesdorff & Bornmann's (2011) Integrated Impact Indicator (I3). As the authors note, the two approaches are akin. Can something be said about the relative quality of the two approaches? To that end, I replicated the study of Mutz & Daniel for the 11 journals in the Subject Category "mathematical psychology," but using additionally I3 on the basis of continuous quantiles (Leydesdorff & Bornmann, in press) and its variant PR6 based on the six percentile rank classes distinguished by Bornmann & Mutz (2011) as follows: the top-1%, 95-99%, 90-95%, 75-90%, 50-75%, and bottom-50%.
On the Implications of Lookahead Search in Game Playing
Venue: arXiv cs.GT 2012
Year: 2012

Lookahead search is perhaps the most natural and widely used game playing strategy. Given the practical importance of the method, the aim of this paper is to provide a theoretical performance examination of lookahead search in a wide variety of applications. To determine a strategy play using lookahead search}, each agent predicts multiple levels of possible re-actions to her move (via the use of a search tree), and then chooses the play that optimizes her future payoff accounting for these re-actions. There are several choices of optimization function the agents can choose, where the most appropriate choice of function will depend on the specifics of the actual game - we illustrate this in our examples. Furthermore, the type of search tree chosen by computationally-constrained agent can vary. We focus on the case where agents can evaluate only a bounded number, $k$, of moves into the future. That is, we use depth $k$ search trees and call this approach {\em k-lookahead search}. We apply our method in five well-known settings: AdWord auctions; industrial organization (Cournot's model); congestion games; valid-utility games and basic-utility games; cost-sharing network design games. We consider two questions. First, what is the expected social quality of outcome when agents apply lookahead search? Second, what interactive behaviours can be exhibited when players use lookahead search?
Equivalence of Games with Probabilistic Uncertainty and Partial-observation Games
Venue: arXiv cs.GT 2012
Year: 2012

We introduce games with probabilistic uncertainty, a natural model for controller synthesis in which the controller observes the state of the system through imprecise sensors that provide correct information about the current state with a fixed probability. That is, in each step, the sensors return an observed state, and given the observed state, there is a probability distribution (due to the estimation error) over the actual current state. The controller must base its decision on the observed state (rather than the actual current state, which it does not know). On the other hand, we assume that the environment can perfectly observe the current state. We show that our model can be reduced in polynomial time to standard partial-observation stochastic games, and vice-versa. As a consequence we establish the precise decidability frontier for the new class of games, and for most of the decidable problems establish optimal complexity results.
Towards an efficient prover for the C1 paraconsistent logic
Venue: arXiv cs.LO 2012
Year: 2012

The KE inference system is a tableau method developed by Marco Mondadori which was presented as an improvement, in the computational efficiency sense, over Analytic Tableaux. In the literature, there is no description of a theorem prover based on the KE method for the C1 paraconsistent logic. Paraconsistent logics have several applications, such as in robot control and medicine. These applications could benefit from the existence of such a prover. We present a sound and complete KE system for C1, an informal specification of a strategy for the C1 prover as well as problem families that can be used to evaluate provers for C1. The C1 KE system and the strategy described in this paper will be used to implement a KE based prover for C1, which will be useful for those who study and apply paraconsistent logics.
Exponential Lower Bounds and Separation for Query Rewriting
Venue: arXiv cs.LO 2012
Year: 2012

We establish connections between the size of circuits and formulas computing monotone Boolean functions and the size of first-order and nonrecursive Datalog rewritings for conjunctive queries over OWL 2 QL ontologies. We use known lower bounds and separation results from circuit complexity to prove similar results for the size of rewritings that do not use non-signature constants. For example, we show that, in the worst case, positive existential and nonrecursive Datalog rewritings are exponentially longer than the original queries; nonrecursive Datalog rewritings are in general exponentially more succinct than positive existential rewritings; while first-order rewritings can be superpolynomially more succinct than positive existential rewritings.
A methodology for internal Web ethics
Venue: arXiv cs.CY 2012
Year: 2012

The vigorous impact of the Web in time and space arises from the fact that it motivates massive creation, editing and distribution of information by Users with little knowledge. This unprecedented continuum provides novel opportunities for innovation but also puts under jeopardy its survival as a stable construct that nurtures a complex system of connections. We examine the Web as an ethics determined space by demonstrating Hayek's theory of freedom in a three-leveled Web: technological, contextualized and economic. Our approach accounts for the co-dependence of code and values, and assumes that the Web is a self-contained system that exists in and by itself. This view of internal Web ethics directly connects the concept of freedom with issues like centralization of traffic and data control, rights on visiting log file, custom User profiles and the interplay among function, structure and morality of the Web. It is also demonstrated, in the case of Net Neutrality, that generic freedom-coercion trade-offs are incomplete in treating specific cases at work.
Strong Backdoors to Nested Satisfiability
Venue: arXiv cs.DS 2012
Year: 2012

Knuth (1990) introduced the class of nested formulas and showed that their satisfiability can be decided in polynomial time. We show that, parameterized by the size of a smallest strong backdoor set to the target class of nested formulas, checking the satisfiability of any CNF formula is fixed-parameter tractable. Thus, for any k>0, the satisfiability problem can be solved in polynomial time for any formula F for which there exists a variable set B of size at most k such that for every truth assignment t to B, the formula F[t] is nested; moreover, the degree of the polynomial is independent of k. Our algorithm uses the grid-minor theorem of Robertson and Seymour (1986) to either find that the incidence graph of the formula has bounded treewidth - a case that is solved using model checking for monadic second order logic - or to find many vertex-disjoint obstructions in the incidence graph. For the latter case, new combinatorial arguments are used to find a small backdoor set. Combining both cases leads to an approximation algorithm producing a strong backdoor set whose size is upper bounded by a function of the optimum. Going through all assignments to this set of variables and using Knuth's algorithm, the satisfiability of the input formula is decided.
GPGPU Processing in CUDA Architecture
Venue: arXiv cs.DC 2012
Year: 2012

The future of computation is the Graphical Processing Unit, i.e. the GPU. The promise that the graphics cards have shown in the field of image processing and accelerated rendering of 3D scenes, and the computational capability that these GPUs possess, they are developing into great parallel computing units. It is quite simple to program a graphics processor to perform general parallel tasks. But after understanding the various architectural aspects of the graphics processor, it can be used to perform other taxing tasks as well. In this paper, we will show how CUDA can fully utilize the tremendous power of these GPUs. CUDA is NVIDIA's parallel computing architecture. It enables dramatic increases in computing performance, by harnessing the power of the GPU. This paper talks about CUDA and its architecture. It takes us through a comparison of CUDA C/C++ with other parallel programming languages like OpenCL and DirectCompute. The paper also lists out the common myths about CUDA and how the future seems to be promising for CUDA.
On the complexity of solving initial value problems
Venue: arXiv cs.NA 2012
Year: 2012

In this paper we prove that computing the solution of an initial-value problem $\dot{y}=p(y)$ with initial condition $y(t_0)=y_0\in\R^d$ at time $t_0+T$ with precision $e^{-μ}$ where $p$ is a vector of polynomials can be done in time polynomial in the value of $T$, $μ$ and $Y=\sup_{t_0\leqslant u\leqslant T}\infnorm{y(u)}$. Contrary to existing results, our algorithm works for any vector of polynomials $p$ over any bounded or unbounded domain and has a guaranteed complexity and precision. In particular we do not assume $p$ to be fixed, nor the solution to lie in a compact domain, nor we assume that $p$ has a Lipschitz constant.